Abstract
Background:
This study analyzed the National Health Interview Surveys (NHIS) of 2016 (n = 33,028) and 2017 (n = 26,742) to examine whether e-cigarette use is consistently associated with myocardial infarction (MI) and coronary heart disease (CHD).
Methods:
Surveys were examined separately and pooled. Logistic regression analysis was used, with demographics, e-cigarette use, smoking and risk factors for CHD (hypertension, hypercholesterolemia, and diabetes) being independent variables. Former smokers were subclassified according to quit duration (⩽ 6 and > 6 years).
Results:
For MI, an association was observed with some days e-cigarette (but not daily) use in the 2017 survey (OR: 2.11, 95% CI: 1.14–3.88, p = 0.017). No statistically significant association was observed in the pooled analysis (daily e-cigarette use: OR: 1.35, 95% CI: 0.80–2.27, p = 0.267). For CHD, an association was observed with daily e-cigarette use in the 2016 survey (OR: 1.89, 95% CI: 1.01–3.53, p = 0.047). From the pooled analysis, no association was found between any pattern of e-cigarette use and CHD. In single-year and pooled analysis, both MI and CHD were strongly associated with all patterns of smoking, hypertension, hypercholesterolemia, diabetes, and age.
Conclusions:
The pooled analysis of the 2016 and 2017 NHIS showed no association between e-cigarette use and MI or CHD. The associations between established risk factors, including smoking, and both conditions were remarkably consistent. The inconsistent associations observed in single-year surveys and the cross-sectional design of the NHIS cannot substantiate any link between e-cigarette use and an elevated risk for MI or CHD. Longitudinal studies are needed to explore the effects of e-cigarette use on cardiovascular disease.
Keywords: coronary heart disease, electronic cigarettes, myocardial infarction, National Health Interview Survey, smoking
Introduction
Cigarette smoking is a well-known risk factor for the development of atherosclerosis and cardiovascular disease (CVD).1 Although the precise mechanisms remain uncertain, several harmful constituents in cigarette smoke have been shown to elicit detrimental responses in the endothelium with resulting alterations of hemostasis and increased platelet activation.2–4 This could have significant implications for the initiation and development of atherothrombosis, as already extensively demonstrated with other well-known risk factors for coronary heart disease (CHD) such as hypertension and hypercholesterolemia.5,6 As a consequence, chronic exposure to cigarette smoke can induce a persistent state of activation of the endothelial–coagulative system and abstinence from smoking may result in the amelioration of several endothelial and coagulative abnormalities.7
Electronic cigarettes (e-cigarettes), an emerging alternative to combustible cigarettes, do not contain tobacco, create smoke, or rely on combustion to operate. Although not completely risk free, the level of chemical constituents in e-cigarette aerosol emissions under normal conditions of use have been shown to be substantially lower compared to cigarette smoke.8–10 However, in laboratory studies of human cell lines, incubation with e-cigarette extracts increased the release of inflammatory mediators.11 In addition, e-cigarette aerosol emissions elicited platelet activation, aggregation, and adhesion.12 In mice, chronic whole body exposure to e-cigarette aerosol emissions accelerates aortic stiffness, significantly impairs aortic endothelial function and may lead to impaired cardiac function.13 In acute clinical studies of healthy smokers, e-cigarettes and combustible cigarettes exhibit similar inhibition of endothelial function as measured by flow-mediated dilation of arteries,14 enhanced sympathetic activity,14 and impaired aortic elasticity.15 While these effects are associated with increased cardiac risk,16,17 this is relevant when measured in resting conditions and not after acute intake of a stimulant such as nicotine. In fact, similar acute effects have been observed with nicotine replacement therapies or even caffeine.18–20 The acute sympathetic effects of nicotine could potentially contribute to acute cardiovascular events, especially in those with underlying coronary heart disease.21
In light of the well-established adverse effects of smoking and the potential of e-cigarettes to substitute for smoking as part of a harm reduction strategy, it is important to examine the association between e-cigarettes and heart disease. A recent study presented a pooled analysis of the National Health Interview Survey (NHIS) for 2014 and 2016, a large, annual cross-sectional survey of adults in the US.22 The study found that daily e-cigarette use was associated with having had a myocardial infarction (MI). Well-established risk factors for MI (including hypertension, hypercholesterolemia, diabetes, age, and smoking) were also significantly associated with MI. While the analysis of cross-sectional studies limits the interpretation of association as causation, such studies are valuable considering the time needed to obtain prospective epidemiological data. In addition, consistency is important in establishing associations and causal links between exposure and disease.23 Therefore, this study used data from the most recent NHIS with publicly available datasets (2016 and 2017) to examine the association between e-cigarette use and MI and CHD. Other well-established risk factors for MI and CHD (including cigarette smoking, hypertension, hypercholesterolemia, and diabetes) were examined for consistency in the NHIS 2016 and 2017 datasets.
Methods
Study sample
The NHIS is a survey conducted by the National Center for Health Statistics (NCHS) since 1960. It is a cross-sectional household interview survey of non-institutionalized US civilians.24 The ‘Sample Adult Public Use Files’ of 2016 (n = 33,028) and 2017 (n = 26,742) were used in this analysis, which contains the responses of US adults (aged ⩾ 18 years). The two datasets were analyzed separately and were also pooled and treated as 1 year with a very large sample size. No ethics committee approval was sought for this study because the datasets were anonymized and are publicly available through the US Centers for Disease Control and Prevention website.
Measures
The survey included demographic data of which age, gender, and race were used in this analysis. For race, participants were classified as White, Black or African American, American Indian or Alaska Native (AIAN), Asian, and Multiple Race. Another response option, ‘race group not releasable’ was recorded together with Multiple Race due to the small sample size (n = 157). Hispanics were included in the ‘White’ category.
For CHD, participants were asked ‘Have you ever been told by a doctor or other health professional that you had coronary heart disease?’. Those responding ‘yes’ were classified as having CHD. For MI, participants were asked: ‘Have you ever been told by a doctor or other health professional that you had a heart attack (also called myocardial infarction)?’. Those responding ‘yes’ were classified as having had an MI.
Risk factors for MI and CHD recorded in the surveys were hypertension (participants were asked ‘Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?’), hypercholesterolemia (participants were asked: ‘Have you ever been told by a doctor or other health professional that you had high cholesterol?’) and diabetes (participants were asked ‘Have you ever been told by a doctor or other health professional that you have diabetes or sugar diabetes?’). The responses about diabetes were coded as yes, no, and borderline or prediabetes.
Never smokers were defined in the survey based on a cut-off point of using 100 cigarettes in their life (participants were asked ‘Have you smoked at least 100 cigarettes in your entire life?’). Those responding ‘no’ were classified as never smokers. Those responding ‘yes’ were subsequently asked about current smoking (participants were asked ‘Do you now smoke cigarettes every day, some days, or not at all?’). This question was used to define daily and some days smokers, while former smokers were defined as those responding ‘yes’ to the question about ever smoking and ‘not anymore’ to the question about current smoking. It is important to note that the duration of smoking cessation is negatively associated with CVD and mortality risk.25–30 At the same time, e-cigarette use was rare before 2010, and a very small proportion of former smokers who had quit before 2010 were using e-cigarettes in the US.31,32 To address this, the question ‘How long has it been since you quit smoking cigarettes?’ was used to subclassify former smokers according to quit duration as former smokers of ⩽6 years and former smokers of >6 years. Additional analyses were performed using difference cutoff points (quit duration of 7 years, 8 years, and 10 years).
Ever e-cigarette use was determined by asking: ‘Have you ever used an e-cigarette even one time?’. Those responding ‘no’ were classified as never users. Those responding ‘yes’ were subsequently asked about current e-cigarette use (participants were asked ‘Do you now use e-cigarettes every day, some days, or not at all?’). This question was used to define daily and some days e-cigarette users, while former e-cigarette users were defined as those responding ‘yes’ to the question about ever e-cigarette use and ‘not anymore’ to the question about current e-cigarette use.
For all questions, responses coded in the dataset as ‘refused,’ ‘not ascertained,’ and ‘don’t know’ were excluded from the analysis.
Statistical analysis
Continuous variables were presented as mean (SD) and categorical variables as a number (%). Comparisons between e-cigarette groups were performed using one-way analysis of variance (ANOVA) or cross-tabulations and chi-squared tests. Logistic regression analyses were performed to examine the association between e-cigarette use and MI and CHD. The independent variables that were included in the models were demographics (age, gender, race/ethnicity, e-cigarette use (classified as daily, few days, former, and never used), smoking (same classification as for e-cigarettes), other established risk factors for CVD (hypertension, hypercholesterolemia, and diabetes) and body-mass index (BMI). The reference condition for both e-cigarette use and cigarette smoking was never use. As mentioned above, the smoking status was classified as daily, some days, former of ⩽6 years, former of >6 years, and never smokers, while additional analyses were performing using quit duration cutoff points of 7 years, 8 years, and 10 years.
To further examine the association between e-cigarette use and MI and CHD without considering the effects of smoking, an attempt to perform the same logistic regression analyses among never smokers was performed. This was not successful because of no or a low number of never-smoking daily and some days e-cigarette users who reported having had an MI or having CHD.
All statistical analyses were weighted by primary sampling unit, sampling stratum, and sampling weight, and were performed using Stata v.13.0 (http://www.stata.com) according to recommendations described by the NCHS.24 It should be emphasized that the years being pooled (2016 and 2017) fall within the same sample design period with the same public use design variables.23 Thus the two datasets can be pooled into one dataset and treated as 1 year of data with a very large sample size.24 The pooled analyses were performed to increase the statistical power, considering the low proportion of the population using e-cigarette users (especially daily users). In addition, results were presented separately for each year to examine the consistency in the observed associations between years. According to the NCHS recommendations, the sample weight in the pooled dataset was divided by the number of years that were being pooled (i.e. by two).24
Results
Participant demographics, smoking status, risk factors, and prevalence of MI and CHD are presented from the pooled analysis, separately for each e-cigarette use group, in Table 1. More than 90% of daily and less than 80% of some days e-cigarette users were smokers or former smokers. As observed in previous studies, e-cigarette use, particularly daily e-cigarette use, was far more prevalent in former smokers of ⩽6 years compared with former smokers of >6 years.
Table 1.
Sample characteristics according to e-cigarette use status, NHIS 2016 and 2017 (pooled).
| Variable | E-cigarette use, % (n) | p value | |||
|---|---|---|---|---|---|
| Daily n = 714 | Some days n = 1009 | Former n = 7026 | Never n = 50830 | ||
| Age | 43.3 (15.7) | 41.2 (15.5) | 41.0 (15.6) | 52.2 (18.6) | <0.001 |
| Gender | |||||
| Male | 58.2% (387) | 56.7% (535) | 55.3% (3621) | 46.9% (22441) | <0.001 |
| Female | 41.8% (327) | 43.3% (474) | 44.8% (3405) | 53.1% (28389) | |
| BMI, kg/m2 mean (SD) | 30.6 (14.4) | 29.2 (11.5) | 29.5 (12.1) | 30.4 (14.2) | <0.001 |
| Race | |||||
| White | 84.8% (617) | 82.0% (847) | 82.8% (5902) | 77.4% (40478) | <0.001 |
| Black/African American | 6.2% (35) | 10.2% (80) | 9.1% (567) | 12.9% (5956) | |
| AIAN | 1.7% (13) | 1.2% (11) | 1.3% (99) | 1.1% (539) | |
| Asian | 3.5% (21) | 2.5% (26) | 3.3% (202) | 6.8% (2817) | |
| Multiple/not releasable | 3.8% (28) | 4.2% (45) | 3.5% (256) | 1.9% (1040) | |
| Smoking | |||||
| Daily | 19.6% (154) | 49.3% (545) | 41.6% (3131) | 6.0% (3354) | <0.001 |
| Some days | 12.0% (88) | 16.2% (163) | 10.2% (728) | 2.3% (1182) | |
| Former ⩽6 years |
54.1% (380) | 13.7% (137) | 16.9% (1166) | 4.1% (2177) | |
| Former >6 years | 5.2% (40) | 2.9% (28) | 5.3% (405) | 17.8% (10316) | |
| Never | 9.1% (52) | 17.9% (134) | 25.8% (1580) | 69.7% (33657) | |
| Risk factors for CVD | |||||
| Hypertension | 27.5% (211) | 26.1% (280) | 25.2% (1941) | 31.9% (18567) | <0.001 |
| Hypercholesterolemia | 27.1% (206) | 21.5% (246) | 22.2% (1703) | 28.9% (16243) | <0.001 |
| Prediabetes | 1.9% (18) | 2.4% (25) | 2.2% (155) | 2.8% (1476) | <0.001 |
| Diabetes | 7.6% (53) | 7.3% (85) | 7.1% (528) | 9.8% (5649) | <0.001 |
| MI | 4.2% (32) | 3.3% (44) | 2.7% (232) | 3.2% (1993) | 0.177 |
| CHD | 4.2% (33) | 2.9% (38) | 2.9% (258) | 4.6% (2893) | <0.001 |
AIAN, American Indian or Alaska Native; BMI, body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; MI, myocardial infarction.
Table 2 presents the results of the logistic regression analysis for MI. From the 2016 NHIS, no statistically significant association was observed between MI and any pattern of e-cigarette use (daily, some days, and former e-cigarette use). From the 2017 NHIS, only some days e-cigarette use was associated with having had MI (p = 0.017). From the pooled analysis, no statistically significant association was observed between MI and daily (p = 0.267), some days (p = 0.373), or former e-cigarette use (p = 0.720). All regression models showed consistent strong associations between all established risk factors (cigarette smoking, hypertension, hypercholesterolemia, and diabetes) and having had MI (p < 0.001 for all). For smoking, all use patterns (daily, some days, and former smoking) were significantly associated with MI (p ⩽ 0.001 for all). Compared with former smokers of ⩽6 years, former smokers of >6 years had lower odds of having had an MI (pooled analysis OR: 0.54, 95% CI: 0.43–0.68, p < 0.001). A significant consistent association with MI was also observed for age and gender (p < 0.001 for both), but not for BMI.
Table 2.
Logistic regression analyses of the association between e-cigarette use and myocardial infarction, NHIS 2016 and 2017.
| Myocardial infarction | 2016 |
2017 |
2016 and 2017 (pooled) |
||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | p value | OR | 95% CI | p value | OR | 95% CI | p value | |
| E-cigarette use | |||||||||
| Never (referent) | |||||||||
| Daily | 1.51 | 0.85–2.67 | 0.157 | 1.07 | 0.36–3.16 | 0.907 | 1.35 | 0.80–2.27 | 0.267 |
| Some days | 0.74 | 0.40–1.36 | 0.330 | 2.11 | 1.14–3.88 | 0.017 | 1.22 | 0.78–1.91 | 0.373 |
| Former | 0.89 | 0.65–1.23 | 0.491 | 1.02 | 0.76–1.39 | 0.875 | 0.96 | 0.77–1.20 | 0.720 |
| Smoking | |||||||||
| Never (referent) | |||||||||
| Daily | 3.09 | 2.39–4.01 | <0.001 | 3.20 | 2.48–4.14 | <0.001 | 3.13 | 2.63–3.73 | <0.001 |
| Some days | 2.23 | 1.47–3.37 | <0.001 | 2.73 | 1.79–4.15 | <0.001 | 2.47 | 1.79–3.40 | <0.001 |
| Former ⩽6 years | 3.09 | 2.24–4.25 | <0.001 | 2.54 | 1.79–3.60 | <0.001 | 2.82 | 2.22–3.57 | <0.001 |
| Former >6 years | 1.41 | 1.18–1.69 | <0.001 | 1.63 | 1.34–1.99 | <0.001 | 1.51 | 1.32–1.74 | <0.001 |
| Hypertension | 2.09 | 1.73–2.52 | <0.001 | 2.23 | 1.81–2.76 | <0.001 | 2.16 | 1.87–2.50 | <0.001 |
| Hypercholesterolemia | 2.47 | 2.05–2.97 | <0.001 | 2.55 | 2.12–3.07 | <0.001 | 2.5 | 2.19–2.85 | <0.001 |
| Blood glucose | |||||||||
| Prediabetes | 1.43 | 0.97–2.11 | 0.074 | 1.08 | 0.72–1.61 | 0.716 | 1.23 | 0.93–1.62 | 0.153 |
| Diabetes | 1.90 | 1.56–2.32 | <0.001 | 1.84 | 1.50–2.26 | <0.001 | 1.87 | 1.62–2.15 | <0.001 |
| Gender | |||||||||
| Male (referent) | |||||||||
| Female | 0.45 | 0.38–0.54 | <0.001 | 0.51 | 0.43–0.60 | <0.001 | 0.48 | 0.43–0.54 | <0.001 |
| Race | |||||||||
| White (referent) | |||||||||
| Black/African American | 0.83 | 0.65–1.07 | 0.152 | 1.19 | 0.91–1.55 | 0.195 | 1.00 | 0.84–1.20 | 0.993 |
| AIAN | 2.99 | 1.37–6.50 | 0.006 | 0.45 | 0.16–1.22 | 0.116 | 1.47 | 0.77–2.84 | 0.245 |
| Asian | 0.49 | 0.27–0.86 | 0.014 | 0.84 | 0.50–1.40 | 0.498 | 0.66 | 0.45–0.98 | 0.038 |
| Multiple/not releasable | 1.54 | 0.74–3.21 | 0.252 | 1.00 | 0.59–1.68 | 0.991 | 1.23 | 0.77–1.97 | 0.380 |
| Age (per year) | 1.06 | 1.05–1.07 | <0.001 | 1.06 | 1.05–1.06 | <0.001 | 1.06 | 1.05–1.06 | <0.001 |
| BMI (per unit) | 1.00 | 0.99–1.01 | 0.519 | 1.00 | 1.00–1.01 | 0.367 | 1.00 | 1.00–1.01 | 0.923 |
AIAN, American Indian or Alaska Native; BMI, body mass index.
Table 3 presents the results of the logistic regression analysis for CHD. From the 2016 NHIS, daily e-cigarette use was significantly associated with having CHD (p = 0.047), while no association was observed for some days and former e-cigarette use. From the 2017 NHIS, no pattern of e-cigarette use was associated with having CHD. From the pooled analysis, no pattern of e-cigarette use was associated with having CHD. All regression models showed consistent strong associations between all established risk factors (cigarette smoking, hypertension, hypercholesterolemia, and diabetes) and having CHD. For smoking, all use patterns (daily, some days, and former smoking) were significantly associated with having CHD (p ⩽ 0.001 for all except some days smoking in the 2016 survey where p = 0.024). Compared to former smokers of ⩽6 years, former smokers of >6 years had lower odds of having CHD (pooled analysis OR: 0.73, 95% CI: 0.59–0.91, p = 0.005). A significant consistent association with CHD was also observed for age and gender (p < 0.001 for both), but not for BMI.
Table 3.
Logistic regression analysis of the association between e-cigarette use and coronary heart disease, NHIS 2016 and 2017.
| Coronary heart disease | 2016 |
2017 |
2016 and 2017 (pooled) |
||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | p value | OR | 95% CI | p value | OR | 95% CI | p value | |
| E-cigarette use | |||||||||
| Never (referent) | |||||||||
| Daily | 1.89 | 1.01–3.53 | 0.047 | 0.66 | 0.29–1.48 | 0.312 | 1.31 | 0.79–2.17 | 0.286 |
| Some days | 0.93 | 0.51–1.70 | 0.814 | 1.48 | 0.74–2.94 | 0.268 | 1.13 | 0.70–1.83 | 0.624 |
| Former | 1.06 | 0.79–1.41 | 0.715 | 1.00 | 0.73–1.37 | 0.978 | 1.03 | 0.83–1.28 | 0.772 |
| Smoking | |||||||||
| Never (referent) | |||||||||
| Daily | 1.55 | 1.23–1.96 | <0.001 | 1.94 | 1.51–2.48 | <0.001 | 1.73 | 1.46–2.05 | <0.001 |
| Some days | 1.57 | 1.06–2.32 | 0.024 | 1.96 | 1.30–2.96 | 0.001 | 1.75 | 1.32–2.32 | <0.001 |
| Former ⩽6 years | 1.88 | 1.42–2.48 | <0.001 | 2.06 | 1.47–2.88 | <0.001 | 1.96 | 1.58–2.44 | <0.001 |
| Former >6 years | 1.34 | 1.15–1.55 | <0.001 | 1.53 | 1.30–1.80 | <0.001 | 1.43 | 1.28–1.60 | <0.001 |
| Hypertension | 2.53 | 2.12–3.02 | <0.001 | 2.52 | 2.07–3.07 | <0.001 | 2.53 | 2.22–2.89 | <0.001 |
| Hypercholesterolemia | 2.77 | 2.37–3.24 | <0.001 | 2.43 | 2.06–2.87 | <0.001 | 2.59 | 2.32–2.90 | <0.001 |
| Blood glucose | |||||||||
| Prediabetes | 1.09 | 0.80–1.49 | 0.583 | 1.18 | 0.82–1.72 | 0.368 | 1.14 | 0.90–1.45 | 0.153 |
| Diabetes | 1.74 | 1.46–2.06 | <0.001 | 1.67 | 1.40–1.98 | <0.001 | 1.70 | 1.51–1.93 | <0.001 |
| Gender | |||||||||
| Male (referent) | |||||||||
| Female | 0.56 | 0.49–0.64 | <0.001 | 0.54 | 0.47–0.62 | <0.001 | 0.55 | 0.50–0.61 | <0.001 |
| Race | |||||||||
| White (referent) | |||||||||
| Black/African American | 0.96 | 0.77–1.20 | 0.717 | 1.03 | 0.82–1.28 | 0.819 | 0.99 | 0.85–1.15 | 0.912 |
| AIAN | 1.43 | 0.74–2.75 | 0.288 | 0.23 | 0.07–0.68 | 0.009 | 0.73 | 0.43–1.26 | 0.260 |
| Asian | 0.64 | 0.43–0.97 | 0.036 | 0.93 | 0.64–1.36 | 0.707 | 0.79 | 0.59–1.04 | 0.092 |
| Multiple/not releasable | 1.71 | 0.92–3.18 | 0.089 | 1.03 | 0.62–1.71 | 0.914 | 1.31 | 0.86–2.01 | 0.213 |
| Age (per year) | 1.06 | 1.06–1.07 | <0.001 | 1.07 | 1.06–1.07 | <0.001 | 1.06 | 1.06–1.07 | <0.001 |
| BMI (per unit) | 1.00 | 0.99–1.00 | 0.397 | 1.00 | 0.99–1.01 | 0.878 | 1.00 | 1.00–1.00 | 0.641 |
AIAN, American Indian or Alaska Native; BMI, body mass index.
Supplementary Table 1 presents the association between e-cigarette use and MI and CHD from the pooled analysis using different cutoff points to subclassify former smokers (7 years, 8 years, and 10 years). The results were almost identical to those using a cutoff value of 6 years, with no statistically significant association between e-cigarette use and CVD conditions.
Discussion
This study examined the association between e-cigarette use and two CVD conditions, MI and CHD, in a large cross-sectional study in the US. All major risk factors for these conditions that were recorded in the surveys were included in the analysis. The main findings of the study were that, while all of the risk factors were consistently and significantly associated with both MI and CHD, inconsistent associations with e-cigarette use were observed, with no association found between any pattern of e-cigarette use and MI or CHD from the pooled analysis. This inconsistency, combined with the inherent limitations of cross-sectional surveys, provides no definite or indirect evidence that e-cigarette use is causally linked with heart disease.
A characteristic of this analysis was the highly consistent and statistically significant positive associations between long-established risk factors, namely smoking, hypertension, hypercholesterolemia, diabetes, and age, and both MI and CHD. Findings were similar in both single-year and pooled analysis. This is expected considering the strong epidemiological data that have established the link between these conditions and heart disease. All patterns of smoking (every day, some days, and former smoking) were strongly associated with both disease conditions. This is consistent with the already demonstrated causal effect of smoking on heart disease. The association with former smoking is also expected because some smokers may have quit after developing the disease while quitting smoking is only gradually and over many years reducing the risk of developing heart disease.25–29 The latter was the main reason for subclassifying smokers according to quit duration in the present study, since e-cigarette use is a recent phenomenon and was rarely observed before 2010.32,33 This approach probably provides a useful methodological insight for the future assessment of the association between e-cigarette use and CVD in population studies.
The associations between e-cigarette use and CVD were highly inconsistent and were only observed in single-year surveys for some patterns of use. Unlike daily use, low e-cigarette use intensity (i.e. some days use) was found to be associated with MI in the 2017 survey, which could be characterized as a ‘paradox’. These inconsistent findings could be related to the high prevalence of current or former smoking among e-cigarette users, especially among daily and some days users. Thus, the association between some days (but not daily) e-cigarette use and MI in the 2017 survey and between daily e-cigarette and CHD in the 2016 survey cannot be considered as strong evidence for a causal link.
Another concern that has been expressed about e-cigarettes is dual use (i.e. both smoking and using e-cigarettes). A recent study analyzed the 2016 and 2017 Behavioral Risk Factor Surveillance System and found a statistically significant association between dual use and CVD.34 However, an analysis among never smokers found no association between any pattern of e-cigarette use and CVD. The findings suggest that e-cigarette exposure may be harmful only when added to smoking. The study provided no information on the smoking intensity, smoking duration and smoking patterns before e-cigarette use initiation of dual users compared to exclusive smokers. In addition, established risk factors for CVD such as hypertension and hypercholesterolemia were not included in the analysis. Therefore, the study conclusions need to be interpreted with caution.
It is well known that causal inferences cannot be performed with cross-sectional data, in part because temporal sequencing cannot be easily established.23 In the NHIS surveys, no questions were asked to ascertain when MI or CHD occurred in relation to e-cigarette use initiation. Similarly, no such information was available in the previous study analyzing the 2014 and 2016 datasets.22 In fact, even if a strong association between e-cigarette use and heart disease was observed, still a causal link would be far from being substantiated. This is relevant to the novelty of the product, being widely available in the US market only after 2010.31,32 Therefore, it is possible that some of the participants developed heart disease before e-cigarettes were available, before using them, or after a short period of time using them while having a long history of smoking. In fact, misclassifying a small number of participants as being e-cigarette users before developing MI could erroneously result in an association between e-cigarette use and MI.33 Perhaps an important limitation of the NHIS 2016 and 2017 datasets is that the number of daily e-cigarette users who had MI or CHD was small (n = 32 and n = 33, respectively). A low number of MI cases among daily e-cigarette users was also observed in the pooled 2014 and 2016 surveys (n = 47). This raises the possibility of chance findings that further explain the inconsistent associations. Another limitation is that the classification of participants according to disease status and risk factors for CVD were based on self-report and were not clinically validated. Other factors that are associated with CVD, such as family history, second-hand smoking exposure, and physical activity, were not available or were not clearly defined in the surveys. Finally, it should be mentioned that the study findings do not necessarily suggest that e-cigarette use does not increase the risk of heart disease. E-cigarettes have only been widely available in the past decade, and longer duration of e-cigarette use may affect CVD risk. Therefore, prospective epidemiological studies are needed to comprehensively address this issue, considering the unique characteristic that the vast majority of e-cigarette users are current and former smokers.
In conclusion, no statistically significant association between e-cigarette use and CVD was found from the pooled analysis of the 2016 and 2017 NHIS, and inconsistent associations were observed from the analyses of each year separately. In contrast, strong and consistent associations were observed for all established risk factors for CVD. The results of this study underscore the well-known limitations of cross-sectional observational studies and, combined with the inconsistent associations across different years, suggest that they cannot be relied upon to render sound inferences with regard to any adverse effect of e-cigarette use on CVD and possibly other outcomes. Prospective epidemiological studies will be needed to address this issue, with particular care taken to examine the complex interactions and temporal associations between smoking and e-cigarette use as well as other risk factors.
Supplemental Material
Supplemental material, Supplementary_Table_1_8 for Is e-cigarette use associated with coronary heart disease and myocardial infarction? Insights from the 2016 and 2017 National Health Interview Surveys by Konstantinos E Farsalinos, Riccardo Polosa, Fabio Cibella and Raymond Niaura in Therapeutic Advances in Chronic Disease
Footnotes
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Conflicts of interest statement: The author(s) declared following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Riccardo Polosa has received lecture fees and research funding from Pfizer, GlaxoSmithKline, CV Therapeutics, NeuroSearch A/S, Sandoz, MSD, Boehringer Ingelheim, Novartis, Duska Therapeutics, and Forest Laboratories. He has also served as a consultant for Pfizer, Global Health Alliance for Treatment of Tobacco Dependence, CV Therapeutics, NeuroSearch A/S, Boehringer Ingelheim, Duska Therapeutics, Forest Laboratories, ECITA (Electronic Cigarette Industry Trade Association, in the UK), and Health Diplomat (consulting company that delivers solutions to global health problems with special emphasis on harm minimization). Lecture fees from a number of European e-cigarette industry and trade associations (including FIVAPE in France and FIESEL in Italy) were directly donated to vaper advocacy no-profit organizations on the behalf of RP. RP is also currently a scientific advisor for LIAF, Lega Italiana Anti-Fumo (Italian acronym for Italian Anti-Smoking League) and Head of the European Technical Committee for Standardization on ‘Requirements and test methods for emissions of electronic cigarettes’ (CEN/TC 437; WG4). Konstantinos Farsalinos and Raymond Niaura have no conflicts of interest to report over the past 3 years. Over the past 5 years, Konstantinos Farsalinos has published two studies funded by the non-profit association AEMSA and one study funded by the non-profit association Tennessee Smoke-Free Association.
ORCID iD: Konstantinos E Farsalinos 
https://orcid.org/0000-0001-6839-4710
Supplemental material: Supplemental material for this article is available online.
Contributor Information
Konstantinos E Farsalinos, Onassis Cardiac Surgery Center, Sygrou 356, Kallithea 17674, Greece Department of Pharmacy, University of Patras, Rio, Greece National School of Public Health, Athens, Greece.
Riccardo Polosa, Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy; Center of Excellence for the acceleration of HArm Reduction (CoEHAR), University of Catania, Catania, Italy.
Fabio Cibella, National Research Council of Italy, Institute of Biomedicine and Molecular Immunology, Palermo, Italy.
Raymond Niaura, Departments of Social and Behavioral Science and Epidemiology, College of Global Public Health, New York University, New York, USA.
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Supplementary Materials
Supplemental material, Supplementary_Table_1_8 for Is e-cigarette use associated with coronary heart disease and myocardial infarction? Insights from the 2016 and 2017 National Health Interview Surveys by Konstantinos E Farsalinos, Riccardo Polosa, Fabio Cibella and Raymond Niaura in Therapeutic Advances in Chronic Disease